Handheld electronic device and method for disambiguation of compound text input and for prioritizing compound language solutions according to quantity of text components

ABSTRACT

A handheld electronic device includes a reduced QWERTY keyboard and is enabled with disambiguation software that is operable to disambiguate compound text input. The device is able to assemble language objects in the memory to generate compound language solutions. The device is able to prioritize compound language solutions according to various criteria.

BACKGROUND

1. Field

The disclosed and claimed concept relates generally to handheldelectronic devices and, more particularly, to a handheld electronicdevice having a reduced keyboard and a compound text inputdisambiguation function, and also relates to an associated method.

2. Background Information

Numerous types of handheld electronic devices are known. Examples ofsuch handheld electronic devices include, for instance, personal dataassistants (PDAs), handheld computers, two-way pagers, cellulartelephones, and the like. Many handheld electronic devices also featurewireless communication capability, although many such handheldelectronic devices are stand-alone devices that are functional withoutcommunication with other devices.

Such handheld electronic devices are generally intended to be portable,and thus are of a relatively compact configuration in which keys andother input structures often perform multiple functions under certaincircumstances or may otherwise have multiple aspects or featuresassigned thereto. With advances in technology, handheld electronicdevices are built to have progressively smaller form factors yet haveprogressively greater numbers of applications and features residentthereon. As a practical matter, the keys of a keypad can only be reducedto a certain small size before the keys become relatively unusable. Inorder to enable text entry, however, a keypad must be capable ofentering all twenty-six letters of the Latin alphabet, for instance, aswell as appropriate punctuation and other symbols.

One way of providing numerous letters in a small space has been toprovide a “reduced keyboard” in which multiple letters, symbols, and/ordigits, and the like, are assigned to any given key. For example, atouch-tone telephone includes a reduced keypad by providing twelve keys,of which ten have digits thereon, and of these ten keys eight have Latinletters assigned thereto. For instance, one of the keys includes thedigit “2” as well as the letters “A”, “B”, and “C”. Other known reducedkeyboards have included other arrangements of keys, letters, symbols,digits, and the like. Since a single actuation of such a key potentiallycould be intended by the user to refer to any of the letters “A”, “B”,and “C”, and potentially could also be intended to refer to the digit“2”, the input generally is an ambiguous input and is in need of sometype of disambiguation in order to be useful for text entry purposes.

In order to enable a user to make use of the multiple letters, digits,and the like on any given key, numerous keystroke interpretation systemshave been provided. For instance, a “multi-tap” system allows a user tosubstantially unambiguously specify a particular character on a key bypressing the same key a number of times equivalent to the position ofthe desired character on the key. For example, on the aforementionedtelephone key that includes the letters “ABC”, and the user desires tospecify the letter “C”, the user will press the key three times. Whilesuch multi-tap systems have been generally effective for their intendedpurposes, they nevertheless can require a relatively large number of keyinputs compared with the number of characters that ultimately areoutput.

Another exemplary keystroke interpretation system would include keychording, of which various types exist. For instance, a particularcharacter can be entered by pressing two keys in succession or bypressing and holding first key while pressing a second key. Stillanother exemplary keystroke interpretation system would be a“press-and-hold/press-and-release” interpretation function in which agiven key provides a first result if the key is pressed and immediatelyreleased, and provides a second result if the key is pressed and heldfor a short period of time. While they systems have likewise beengenerally effective for their intended purposes, such systems also havetheir own unique drawbacks.

Another keystroke interpretation system that has been employed is asoftware-based text disambiguation function. In such a system, a usertypically presses keys to which one or more characters have beenassigned, generally pressing each key one time for each desired letter,and the disambiguation software attempt to predict the intended input.Numerous such systems have been proposed, and while many have beengenerally effective for their intended purposes, shortcomings stillexist.

It would be desirable to provide an improved handheld electronic devicewith a reduced keyboard that seeks to mimic a QWERTY keyboard experienceor other particular keyboard experience. Such an improved handheldelectronic device might also desirably be configured with enoughfeatures to enable text entry and other tasks with relative ease.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding can be gained from the following Description whenread in conjunction with the accompanying drawings in which:

FIG. 1 is a top plan view of an improved handheld electronic device inaccordance with the disclosed and claimed concept;

FIG. 2 is a schematic depiction of the improved handheld electronicdevice of FIG. 1;

FIG. 2A is a schematic depiction of a portion of the handheld electronicdevice of FIG. 2;

FIGS. 3A and 3B are an exemplary flowchart depicting certain aspects ofa disambiguation function that can be executed on the handheldelectronic device of FIG. 1;

FIG. 4 is another exemplary flowchart depicting certain aspects of adisambiguation function that can be executed on the handheld electronicdevice by which certain output variants can be provided to the user;

FIGS. 5A and SB are another exemplary flowchart depicting certainaspects of a learning method that can be executed on the handheldelectronic device;

FIG. 6 is another exemplary flowchart depicting certain aspects of amethod by which various display formats can be provided on the handheldelectronic device;

FIG. 7 is an exemplary output during a text entry operation;

FIG. 8 is another exemplary output during another part of the text entryoperation;

FIG. 9 is another exemplary output during another part of the text entryoperation;

FIG. 10 is another exemplary output during another part of the textentry operation;

FIG. 11 is an exemplary output on the handheld electronic device duringanother text entry operation;

FIG. 12 is an exemplary output that can be provided in an instance whenthe disambiguation function of the handheld electronic device has beendisabled;

FIG. 13 is a schematic depiction of an ambiguous input to the handheldelectronic device of FIG. 1;

FIG. 13A is a schematic depiction of the ambiguous input of FIG. 13 inaccordance with a compound language solution;

FIG. 13B is a schematic depiction of the ambiguous input of FIG. 13 inaccordance with another compound language solution;

FIG. 13C is a schematic depiction of the ambiguous input of FIG. 13 inaccordance with another compound language solution;

FIG. 13D is a schematic depiction of the ambiguous input of FIG. 13 inaccordance with another compound language solution;

FIG. 13E is a schematic depiction of the ambiguous input of FIG. 13 inaccordance with another compound language solution;

FIG. 13F is a schematic depiction of the ambiguous input of FIG. 13 inaccordance with another compound language solution;

FIG. 13G is a schematic depiction of the ambiguous input of FIG. 13 inaccordance with another compound language solution;

FIG. 14 is a schematic depiction of an output of a representation of atleast a portion of a compound language solution;

FIGS. 15A, 15B, and 15C are another exemplary flowchart depictingcertain aspects of a method that can be executed on the handheldelectronic device; and

FIG. 16 is a schematic depiction of another ambiguous input to thehandheld electronic device of FIG. 1.

Similar numerals refer to similar parts throughout the specification.

DESCRIPTION

An improved handheld electronic device 4 is indicated generally in FIG.1 and is depicted schematically in FIG. 2. The exemplary handheldelectronic device 4 includes a housing 6 upon which are disposed aprocessor unit that includes an input apparatus 8, an output apparatus12, a processor 16, a memory 20, and at least a first routine. Theprocessor 16 may be, for instance, and without limitation, amicroprocessor (μP) and is responsive to inputs from the input apparatus8 and provides output signals to the output apparatus 12. The processor16 also interfaces with the memory 20. The processor 16 and the memory20 together form a processor apparatus. Examples of handheld electronicdevices are included in U.S. Pat. Nos. 6,452,588 and 6,489,950, whichare incorporated by record herein.

As can be understood from FIG. 1, the input apparatus 8 includes akeypad 24 and a thumbwheel 32. As will be described in greater detailbelow, the keypad 24 is in the exemplary form of a reduced QWERTYkeyboard including a plurality of keys 28 that serve as input members.It is noted, however, that the keypad 24 may be of other configurations,such as an AZERTY keyboard, a QWERTZ keyboard, or other keyboardarrangement, whether presently known or unknown, and either reduced ornot reduced. As employed herein, the expression “reduced” and variationsthereof in the context of a keyboard, a keypad, or other arrangement ofinput members, shall refer broadly to an arrangement in which at leastone of the input members has assigned thereto a plurality of linguisticelements such as, for example, characters in the set of Latin letters,whereby an actuation of the at least one of the input members, withoutanother input in combination therewith, is an ambiguous input since itcould refer to more than one of the plurality of linguistic elementsassigned thereto. As employed herein, the expression “linguisticelement” and variations thereof shall refer broadly to any element thatitself can be a language object or from which a language object can beconstructed, identified, or otherwise obtained, and thus would include,for example and without limitation, characters, letters, strokes,ideograms, phonemes, morphemes, digits, and the like. As employedherein, the expression “language object” and variations thereof shallrefer broadly to any type of object that may be constructed, identified,or otherwise obtained from one or more linguistic elements, that can beused alone or in combination to generate text, and that would include,for example and without limitation, words, shortcuts, symbols,ideograms, and the like.

The system architecture of the handheld electronic device 4advantageously is organized to be operable independent of the specificlayout of the keypad 24. Accordingly, the system architecture of thehandheld electronic device 4 can be employed in conjunction withvirtually any keypad layout substantially without requiring anymeaningful change in the system architecture. It is further noted thatcertain of the features set forth herein are usable on either or both ofa reduced keyboard and a non-reduced keyboard.

The keys 28 are disposed on a front face of the housing 6, and thethumbwheel 32 is disposed at a side of the housing 6. The thumbwheel 32can serve as another input member and is both rotatable, as is indicatedby the arrow 34, to provide selection inputs to the processor 16, andalso can be pressed in a direction generally toward the housing 6, as isindicated by the arrow 38, to provide another selection input to theprocessor 16.

Among the keys 28 of the keypad 24 are a <NEXT> key 40 and an <ENTER>key 44. The <NEXT> key 40 can be pressed to provide a selection input tothe processor 16 and provides substantially the same selection input asis provided by a rotational input of the thumbwheel 32. Since the <NEXT>key 40 is provided adjacent a number of the other keys 28 of the keypad24, the user can provide a selection input to the processor 16substantially without moving the user's hands away from the keypad 24during a text entry operation. As will be described in greater detailbelow, the <NEXT> key 40 additionally and advantageously includes agraphic 42 disposed thereon, and in certain circumstances the outputapparatus 12 also displays a displayed graphic 46 thereon to identifythe <NEXT> key 40 as being able to provide a selection input to theprocessor 16. In this regard, the displayed graphic 46 of the outputapparatus 12 is substantially similar to the graphic 42 on the <NEXT>key and thus identifies the <NEXT> key 40 as being capable of providinga desirable selection input to the processor 16.

As can further be seen in FIG. 1, many of the keys 28 include a numberof linguistic elements 48 disposed thereon. As employed herein, theexpression “a number of” and variations thereof shall refer broadly toany quantity, including a quantity of one, and in certain circumstancesherein can also refer to a quantity of zero. In the exemplary depictionof the keypad 24, many of the keys 28 include two linguistic elements,such as including a first linguistic element 52 and a second linguisticelement 56 assigned thereto.

One of the keys 28 of the keypad 24 includes as the characters 48thereof the letters “Q” and “W”, and an adjacent key 28 includes as thecharacters 48 thereof the letters “E” and “R”. It can be seen that thearrangement of the characters 48 on the keys 28 of the keypad 24 isgenerally of a QWERTY arrangement, albeit with many of the keys 28including two of the characters 48.

The output apparatus 12 includes a display 60 upon which can be providedan output 64. An exemplary output 64 is depicted on the display 60 inFIG. 1. The output 64 includes a text component 68 and a variantcomponent 72. The variant component 72 includes a default portion 76 anda variant portion 80. The display also includes a caret 84 that depictsgenerally where the next input from the input apparatus 8 will bereceived.

The text component 68 of the output 64 provides a depiction of thedefault portion 76 of the output 64 at a location on the display 60where the text is being input. The variant component 72 is disposedgenerally in the vicinity of the text component 68 and provides, inaddition to the default proposed output 76, a depiction of the variousalternate text choices, i.e., alternates to the default proposed output76, that are proposed by an input disambiguation function in response toan input sequence of key actuations of the keys 28.

As will be described in greater detail below, the default portion 76 isproposed by the disambiguation function as being the most likelydisambiguated interpretation of the ambiguous input provided by theuser. The variant portion 80 includes a predetermined quantity ofalternate proposed interpretations of the same ambiguous input fromwhich the user can select, if desired. The displayed graphic 46typically is provided in the variant component 72 in the vicinity of thevariant portion 80, although it is understood that the displayed graphic46 could be provided in other locations and in other fashions. It isalso noted that the exemplary variant portion 80 is depicted herein asextending vertically below the default portion 76, but it is understoodthat numerous other arrangements could be provided.

Among the keys 28 of the keypad 24 additionally is a <DELETE> key 86that can be provided to delete a text entry. As will be described ingreater detail below, the <DELETE> key 86 can also be employed inproviding an alternation input to the processor 16 for use by thedisambiguation function.

The memory 20 is depicted schematically in FIG. 2A. The memory 20 can beany of a variety of types of internal and/or external storage media suchas, without limitation, RAM, ROM, EPROM(s), EEPROM(s), and the like thatprovide a storage register for data storage such as in the fashion of aninternal storage area of a computer, and can be volatile memory ornonvolatile memory. The memory 20 additionally includes a number ofroutines depicted generally with the numeral 22 for the processing ofdata. The routines 22 can be in any of a variety of forms such as,without limitation, software, firmware, and the like. As will beexplained in greater detail below, the routines 22 include theaforementioned disambiguation function as an application, as well asother routines.

As can be understood from FIG. 2A, the memory 20 additionally includesdata stored and/or organized in a number of tables, sets, lists, and/orotherwise. Specifically, the memory 20 includes a generic word list 88,a new words database 92, and a frequency learning database 96. Storedwithin the various areas of the memory 20 are a number of languageobjects 100 and frequency objects 104. The language objects 100generally are each associated with an associated frequency object 104.The language objects 100 include, in the present exemplary embodiment, aplurality of word objects 108 and a plurality of N-gram objects 112. Theword objects 108 are generally representative of complete words withinthe language or custom words stored in the memory 20. For instance, ifthe language stored in the memory 20 is, for example, English, generallyeach word object 108 would represent a word in the English language orwould represent a custom word.

Associated with substantially each word object 108 is a frequency object104 having frequency value that is indicative of the relative frequencywithin the relevant language of the given word represented by the wordobject 108. In this regard, the generic word list 88 includes a corpusof word objects 108 and associated frequency objects 104 that togetherare representative of a wide variety of words and their relativefrequency within a given vernacular of, for instance, a given language.The generic word list 88 can be derived in any of a wide variety offashions, such as by analyzing numerous texts and other language sourcesto determine the various words within the language sources as well astheir relative probabilities, i.e., relative frequencies, of occurrencesof the various words within the language sources.

The N-gram objects 112 stored within the generic word list 88 are shortstrings of characters within the relevant language typically, forexample, one to three characters in length, and typically represent wordfragments within the relevant language, although certain of the N-gramobjects 112 additionally can themselves be words. However, to the extentthat an N-gram object 112 also is a word within the relevant language,the same word likely would be separately stored as a word object 108within the generic word list 88. As employed herein, the expression“string” and variations thereof shall refer broadly to an object havingone or more characters or components, and can refer to any of a completeword, a fragment of a word, a custom word or expression, and the like.

In the present exemplary embodiment of the handheld electronic device 4,the N-gram objects 112 include 1-gram objects, i.e., string objects thatare one character in length, 2-gram objects, i.e., string objects thatare two characters in length, and 3-gram objects, i.e., string objectsthat are three characters in length, all of which are collectivelyreferred to as N-grams 112. Substantially each N-gram object 112 in thegeneric word list 88 is similarly associated with an associatedfrequency object 104 stored within the generic word list 88, but thefrequency object 104 associated with a given N-gram object 112 has afrequency value that indicates the relative probability that thecharacter string represented by the particular N-gram object 112 existsat any location within any word of the relevant language. The N-gramobjects 112 and the associated frequency objects 104 are a part of thecorpus of the generic word list 88 and are obtained in a fashion similarto the way in which the word object 108 and the associated frequencyobjects 104 are obtained, although the analysis performed in obtainingthe N-gram objects 112 will be slightly different because it willinvolve analysis of the various character strings within the variouswords instead of relying primarily on the relative occurrence of a givenword.

The present exemplary embodiment of the handheld electronic device 4,with its exemplary language being the English language, includestwenty-six 1-gram N-gram objects 112, i.e., one 1-gram object for eachof the twenty-six letters in the Latin alphabet upon which the Englishlanguage is based, and further includes 676 2-gram N-gram objects 112,i.e., twenty-six squared, representing each two-letter permutation ofthe twenty-six letters within the Latin alphabet.

The N-gram objects 112 also include a certain quantity of 3-gram N-gramobjects 112, primarily those that have a relatively high frequencywithin the relevant language. The exemplary embodiment of the handheldelectronic device 4 includes fewer than all of the three-letterpermutations of the twenty-six letters of the Latin alphabet due toconsiderations of data storage size, and also because the 2-gram N-gramobjects 112 can already provide a meaningful amount of informationregarding the relevant language. As will be set forth in greater detailbelow, the N-gram objects 112 and their associated frequency objects 104provide frequency data that can be attributed to character strings forwhich a corresponding word object 108 cannot be identified or has notbeen identified, and typically is employed as a fallback data source,although this need not be exclusively the case.

In the present exemplary embodiment, the language objects 100 and thefrequency objects 104 are maintained substantially inviolate in thegeneric word list 88, meaning that the basic language corpus remainssubstantially unaltered within the generic word list 88, and thelearning functions that are provided by the handheld electronic device 4and that are described below operate in conjunction with other objectthat are generally stored elsewhere in memory 20, such as, for example,in the new words database 92 and the frequency learning database 96.

The new words database 92 and the frequency learning database 96 storeadditional word objects 108 and associated frequency objects 104 inorder to provide to a user a customized experience in which words andthe like that are used relatively more frequently by a user will beassociated with relatively higher frequency values than might otherwisebe reflected in the generic word list 88. More particularly, the newwords database 92 includes word objects 108 that are user-defined andthat generally are not found among the word objects 108 of the genericword list 88. Each word object 108 in the new words database 92 hasassociated therewith an associated frequency object 104 that is alsostored in the new words database 92. The frequency learning database 96stores word objects 108 and associated frequency objects 104 that areindicative of relatively more frequent usage of such words by a userthan would be reflected in the generic word list 88. As such, the newwords database 92 and the frequency learning database 96 provide twolearning functions, that is, they together provide the ability to learnnew words as well the ability to learn altered frequency values forknown words.

FIGS. 3A and 3B depicts in an exemplary fashion the general operation ofcertain aspects of the disambiguation function of the handheldelectronic device 4. Additional features, functions, and the like aredepicted and described elsewhere.

An input is detected, as at 204, and the input can be any type ofactuation or other operation as to any portion of the input apparatus 8.A typical input would include, for instance, an actuation of a key 28having a number of characters 48 thereon, or any other type of actuationor manipulation of the input apparatus 8.

Upon detection at 204 of an input, a timer is reset at 208. The use ofthe timer will be described in greater detail below.

The disambiguation function then determines, as at 212, whether thecurrent input is an operational input, such as a selection input, adelimiter input, a movement input, an alternation input, or, forinstance, any other input that does not constitute an actuation of a key28 having a number of characters 48 thereon. If the input is determinedat 212 to not be an operational input, processing continues at 216 byadding the input to the current input sequence which may or may notalready include an input.

Many of the inputs detected at 204 are employed in generating inputsequences as to which the disambiguation function will be executed. Aninput sequence is build up in each “session” with each actuation of akey 28 having a number of characters 48 thereon. Since an input sequencetypically will be made up of at least one actuation of a key 28 having aplurality of characters 48 thereon, the input sequence will beambiguous. When a word, for example, is completed the current session isended an a new session is initiated.

An input sequence is gradually built up on the handheld electronicdevice 4 with each successive actuation of a key 28 during any givensession. Specifically, once a delimiter input is detected during anygiven session, the session is terminated and a new session is initiated.Each input resulting from an actuation of one of the keys 28 having anumber of the characters 48 associated therewith is sequentially addedto the current input sequence. As the input sequence grows during agiven session, the disambiguation function generally is executed witheach actuation of a key 28, i.e., and input, and as to the entire inputsequence. Stated otherwise, within a given session, the growing inputsequence is attempted to be disambiguated as a unit by thedisambiguation function with each successive actuation of the variouskeys 28.

Once a current input representing a most recent actuation of the one ofthe keys 28 having a number of the characters 48 assigned thereto hasbeen added to the current input sequence within the current session, asat 216 in FIG. 3A, the disambiguation function generates, as at 220,substantially all of the permutations of the characters 48 assigned tothe various keys 28 that were actuated in generating the input sequence.In this regard, the “permutations” refer to the various strings that canresult from the characters 48 of each actuated key 28 limited by theorder in which the keys 28 were actuated. The various permutations ofthe characters in the input sequence are employed as prefix objects.

For instance, if the current input sequence within the current sessionis the ambiguous input of the keys “AS” and “OP”, the variouspermutations of the first character 52 and the second character 56 ofeach of the two keys 28, when considered in the sequence in which thekeys 28 were actuated, would be “SO”, “SP”, “AP”, and “AO”, and each ofthese is a prefix object that is generated, as at 220, with respect tothe current input sequence. As will be explained in greater detailbelow, the disambiguation function seeks to identify for each prefixobject one of the word objects 108 for which the prefix object would bea prefix.

For each generated prefix object, the memory 20 is consulted, as at 224,to identify, if possible, for each prefix object one of the word objects108 in the memory 20 that corresponds with the prefix object, meaningthat the sequence of letters represented by the prefix object would beeither a prefix of the identified word object 108 or would besubstantially identical to the entirety of the word object 108. Furtherin this regard, the word object 108 that is sought to be identified isthe highest frequency word object 108. That is, the disambiguationfunction seeks to identify the word object 108 that corresponds with theprefix object and that also is associated with a frequency object 104having a relatively higher frequency value than any of the otherfrequency objects 104 associated with the other word objects 108 thatcorrespond with the prefix object.

It is noted in this regard that the word objects 108 in the generic wordlist 88 are generally organized in data tables that correspond with thefirst two letters of various words. For instance, the data tableassociated with the prefix “CO” would include all of the words such as“CODE”, “COIN”, “COMMUNICATION”, and the like. Depending upon thequantity of word objects 108 within any given data table, the data tablemay additionally include sub-data tables within which word objects 108are organized by prefixes that are three characters or more in length.Continuing onward with the foregoing example, if the “CO” data tableincluded, for instance, more than 256 word objects 108, the “CO” datatable would additionally include one or more sub-data tables of wordobjects 108 corresponding with the most frequently appearingthree-letter prefixes. By way of example, therefore, the “CO” data tablemay also include a “COM” sub-data table and a “CON” sub-data table. If asub-data table includes more than the predetermined number of wordobjects 108, for example a quantity of 256, the sub-data table mayinclude further sub-data tables, such as might be organized according toa four letter prefixes. It is noted that the aforementioned quantity of256 of the word objects 108 corresponds with the greatest numericalvalue that can be stored within one byte of the memory 20.

Accordingly, when, at 224, each prefix object is sought to be used toidentify a corresponding word object 108, and for instance the instantprefix object is “AP”, the “AP” data table will be consulted. Since allof the word objects 108 in the “AP” data table will correspond with theprefix object “AP”, the word object 108 in the “AP” data table withwhich is associated a frequency object 104 having a frequency valuerelatively higher than any of the other frequency objects 104 in the“AP” data table is identified. The identified word object 108 and theassociated frequency object 104 are then stored in a result registerthat serves as a result of the various comparisons of the generatedprefix objects with the contents of the memory 20.

It is noted that one or more, or possibly all, of the prefix objectswill be prefix objects for which a corresponding word object 108 is notidentified in the memory 20. Such prefix objects are considered to beorphan prefix objects and are separately stored or are otherwiseretained for possible future use. In this regard, it is noted that manyor all of the prefix objects can become orphan object if, for instance,the user is trying to enter a new word or, for example, if the user hasmis-keyed and no word corresponds with the mis-keyed input.

Once the result has been obtained at 224, the disambiguation function 22determines, as at 225, whether at least one language object 100 wasidentified as corresponding with a prefix object. If not, processingcontinues as at 226 where processing branches to FIG. 15A, which isdiscussed in greater detail elsewhere herein. If it is determined at 225that at least one language object 100 was identified as correspondingwith a prefix object, processing continues at 228 where thedisambiguation routine 22 begins to determine whether artificialvariants should be generated.

In order to determine the need for artificial variants, the process at228 branches, as at 230, to the artificial variant process depictedgenerally in FIG. 4 and beginning with the numeral 304. Thedisambiguation function then determines, as at 308, whether any of theprefix objects in the result correspond with what had been the defaultoutput 76 prior to detection of the current key input. If a prefixobject in the result corresponds with the previous default output, thismeans that the current input sequence corresponds with a word object 108and, necessarily, the previous default output also corresponded with aword object 108 during the previous disambiguation cycle within thecurrent session.

If it is determined at 308 that a prefix object in the resultcorresponds with what had been the default output 76 prior to detectionof the current key input, the next point of analysis is to determine, asat 310, whether the previous default output was made the default outputbecause of a selection input, such as would have caused the setting of aflag, such as at 254 of FIG. 3B, discussed in greater detail elsewhereherein. In the event that the previous default output was not the resultof a selection input, meaning that no flag was set, no artificialvariants are needed, and the process returns, as at 312, to the mainprocess at 232. However, if it is determined at 310 that the previousdefault output was the result of a selection input, then artificialvariants are generated, as at 316.

More specifically, each of the artificial variants generated at 316include the previous default output plus one of the characters 48assigned to the key 28 of the current input. As such, if the key 28 ofthe current input has two characters, i.e., a first character 52 and asecond character 56, two artificial variants will be generated at 316.One of the artificial variants will include the previous default outputplus the first character 52. The other artificial variant will includethe previous default output plus the second character 56.

However, if it is determined at 308 that none of the prefix objects inthe result correspond with the previous default output, it is nextnecessary to determine, as at 314, whether the previous default outputhad corresponded with a word object 108 during the previousdisambiguation cycle within the current session. If the answer to theinquiry at 314 is no, it is still necessary to determine, as at 318,whether the previous default output was made the default output becauseof a selection input, such as would have causes the setting of the flag.In the event that the previous default output was not the result of aselection input, no artificial variants are needed, and the processreturns, as at 312, to the main process at 232.

However, if it is determined at 318 that the previous default output wasthe result of a selection input, it is necessary to next determine as at319 whether the pre-selection default output, i.e., what had been thedefault output prior to the selection input that was identified at 318,corresponded with a word object 108. If so, artificial variants arecreated, as at 321, for the pre-selection default output plus each ofthe linguistic elements assigned to the key 28 of the current input.Processing thereafter continues to 316 where artificial variants aregenerated for the previous default output plus the linguistic elementsassigned to the key 28 of the current input. Alternatively, if at 319 itis determined that the pre-selection default output did not correspondwith a word object 108, processing continues directly to 316 whereartificial variants are generated for the previous default output plusthe linguistic elements assigned to the key 28 of the current input.

On the other hand, if it is determined that the answer to the inquiry at314 is yes, meaning that the previous default output had correspondedwith a word object, but with the current input the previous defaultoutput combined with the current input has ceased to correspond with anyword object 108, then artificial variants are generated, again as at316.

After the artificial variants are generated at 316, the method thendetermines, as at 320, whether the result includes any prefix objects atall. If not, processing returns, as at 312, to the main process at 232.However, if it is determined at 320 that the result includes at least afirst prefix object, meaning that the current input sequence correspondswith a word object 108, processing is transferred to 324 where anadditional artificial variant is created. Specifically, the prefixobject of the result with which is associated the frequency object 104having the relatively highest frequency value among the other frequencyobjects 104 in the result is identified, and the artificial variant iscreated by deleting the final character from the identified prefixobject and replacing it with an opposite character 48 on the same key 28of the current input that generated the final character 48 of theidentified prefix object. In the event that the specific key 28 has morethan two characters 48 assigned thereto, each such opposite character 48will be used to generate an additional artificial variant.

Once the need for artificial variants has been identified, as at 228,and such artificial variants have been generated, as in FIG. 4 and asdescribed above, processing continues, as at 232, where duplicate wordobjects 108 associated with relatively lower frequency values aredeleted from the result. Such a duplicate word object 108 could begenerated, for instance, by the frequency learning database 96, as willbe set forth in greater detail below. If a word object 108 in the resultmatches one of the artificial variants, the word object 108 and itsassociated frequency object 104 generally will be removed from theresult because the artificial variant will be assigned a preferredstatus in the output 64, likely in a position preferred to any wordobject 108 that might have been identified.

Once the duplicate word objects 108 and the associated frequency objects104 have been removed at 232, the remaining prefix objects are arranged,as at 236, in an output set in decreasing order of frequency value. Theorphan prefix objects mentioned above may also be added to the outputset, albeit at positions of relatively lower frequency value than anyprefix object for which a corresponding word object 108 was found. It isalso necessary to ensure that the artificial variants, if they have beencreated, are placed at a preferred position in the output set. It isunderstood that artificial variants may, but need not necessarily be,given a position of preference, i.e., assigned a relatively higherpriority or frequency, than prefix objects of the result.

If it is determined, as at 240, that the flag has been set, meaning thata user has made a selection input, either through an express selectioninput or through an alternation input of a movement input, then thedefault output 76 is considered to be “locked,” meaning that theselected variant will be the default prefix until the end of thesession. If it is determined at 240 that the flag has been set, theprocessing will proceed to 244 where the contents of the output set willbe altered, if needed, to provide as the default output 76 an outputthat includes the selected prefix object, whether it corresponds with aword object 108 or is an artificial variant. In this regard, it isunderstood that the flag can be set additional times during a session,in which case the selected prefix associated with resetting of the flagthereafter becomes the “locked” default output 76 until the end of thesession or until another selection input is detected.

Processing then continues, as at 248, to an output step after which anoutput 64 is generated as described above. More specifically, processingproceeds, as at 250, to the subsystem depicted generally in FIG. 6 anddescribed below. Processing thereafter continues at 204 where additionalinput is detected. On the other hand, if it is determined at 240 thatthe flag had not been set, then processing goes directly to 248 withoutthe alteration of the contents of the output set at 244.

The handheld electronic device 4 may be configured such that any orphanprefix object that is included in an output 64 but that is not selectedwith the next input is suspended. This may be limited to orphan prefixobjects appearing in the variant portion 80 or may apply to orphanprefix objects anywhere in the output 64. The handheld electronic device4 may also be configured to similarly suspend artificial variants insimilar circumstances. A reason for such suspension is that each suchorphan prefix object and/or artificial variant, as appropriate, mayspawn a quantity of offspring orphan prefix objects equal to thequantity of characters 48 on a key 28 of the next input. That is, eachoffspring will include the parent orphan prefix object or artificialvariant plus one of the characters 48 of the key 28 of the next input.Since orphan prefix objects and artificial variants substantially do nothave correspondence with a word object 108, spawned offspring objectsfrom parent orphan prefix objects and artificial variants likewise willnot have correspondence with a word object 108. Such suspended orphanprefix objects and/or artificial variants may be considered to besuspended, as compared with being wholly eliminated, since suchsuspended orphan prefix objects and/or artificial variants may reappearlater as parents of a spawned orphan prefix objects and/or artificialvariants, as will be explained below.

If the detected input is determined, as at 212, to be an operationalinput, processing then continues to determine the specific nature of theoperational input. For instance, if it is determined, as at 252, thatthe current input is a selection input, processing continues at 254. At254, the word object 108 and the associated frequency object 104 of thedefault portion 76 of the output 64, as well as the word object 108 andthe associated frequency object 104 of the portion of the variant output80 that was selected by the selection input, are stored in a temporarylearning data register. Additionally, the flag is set. Processing thenreturns to detection of additional inputs as at 204.

If it is determined, as at 260, that the input is a delimiter input,processing continues at 264 where the current session is terminated andprocessing is transferred, as at 266, to the learning functionsubsystem, as at 404 of FIG. 5A. A delimiter input would include, forexample, the actuation of a <SPACE> key 116, which would both enter adelimiter symbol and would add a space at the end of the word, actuationof the <ENTER> key 44, which might similarly enter a delimiter input andenter a space, and by a translation of the thumbwheel 32, such as isindicated by the arrow 38, which might enter a delimiter input withoutadditionally entering a space.

It is first determined, as at 408, whether the default output at thetime of the detection of the delimiter input at 260 matches a wordobject 108 in the memory 20. If it does not, this means that the defaultoutput is a user-created output that should be added to the new wordsdatabase 92 for future use. In such a circumstance processing thenproceeds to 412 where the default output is stored in the new wordsdatabase 92 as a new word object 108. Additionally, a frequency object104 is stored in the new words database 92 and is associated with theaforementioned new word object 108. The new frequency object 104 isgiven a relatively high frequency value, typically within the upperone-fourth or one-third of a predetermined range of possible frequencyvalues.

In this regard, frequency objects 104 are given an absolute frequencyvalue generally in the range of zero to 65,535. The maximum valuerepresents the largest number that can be stored within two bytes of thememory 20. The new frequency object 104 that is stored in the new wordsdatabase 92 is assigned an absolute frequency value within the upperone-fourth or one-third of this range, particularly since the new wordwas used by a user and is likely to be used again.

With further regard to frequency object 104, it is noted that within agiven data table, such as the “CO” data table mentioned above, theabsolute frequency value is stored only for the frequency object 104having the highest frequency value within the data table. All of theother frequency objects 104 in the same data table have frequency valuesstored as percentage values normalized to the aforementioned maximumabsolute frequency value. That is, after identification of the frequencyobject 104 having the highest frequency value within a given data table,all of the other frequency objects 104 in the same data table areassigned a percentage of the absolute maximum value, which representsthe ratio of the relatively smaller absolute frequency value of aparticular frequency object 104 to the absolute frequency value of theaforementioned highest value frequency object 104. Advantageously, suchpercentage values can be stored within a single byte of memory, thussaving storage space within the handheld electronic device 4.

Upon creation of the new word object 108 and the new frequency object104, and storage thereof within the new words database 92, processing istransferred to 420 where the learning process is terminated. Processingis then returned to the main process, as at 204.

If at 408 it is determined that the word object 108 in the defaultoutput 76 matches a word object 108 within the memory 20, processingthen continues at 416 where it is determined whether the aforementionedflag has been set, such as occurs upon the detection of a selectioninput, and alternation input, or a movement input, by way of example. Ifit turns out that the flag has not been set, this means that the userhas not expressed a preference for a variant prefix object over adefault prefix object, and no need for frequency learning has arisen. Insuch a circumstance, processing continues at 420 where the learningprocess is terminated. Processing then returns to the main process at204.

However, if it is determined at 416 that the flag has been set, theprocessor 20 retrieves from the temporary learning data register themost recently saved default and variant word objects 108, along withtheir associated frequency objects 104. It is then determined, as at428, whether the default and variant word objects 108 had previouslybeen subject of a frequency learning operation. This might bedetermined, for instance, by determining whether the variant word object108 and the associated frequency object 104 were obtained from thefrequency learning database 96. If the default and variant word objects108 had not previously been the subject of a frequency learningoperation, processing continues, as at 432, where the variant wordobject 108 is stored in the frequency learning database 96, and arevised frequency object 104 is generated having a frequency valuegreater than that of the frequency object 104 that previously had beenassociated with the variant word object 108. In the present exemplarycircumstance, i.e., where the default word object 108 and the variantword object 108 are experiencing their first frequency learningoperation, the revised frequency object 104 may, for instance, be givena frequency value equal to the sum of the frequency value of thefrequency object 104 previously associated with the variant word object108 plus one-half the difference between the frequency value of thefrequency object 104 associated with the default word object 108 and thefrequency value of the frequency object 104 previously associated withthe variant word object 108. Upon storing the variant word object 108and the revised frequency object 104 in the frequency learning database96, processing continues at 420 where the learning process is terminatedand processing returns to the main process, as at 204.

If it is determined at 428 that that default word object 108 and thevariant word object 108 had previously been the subject of a frequencylearning operation, processing continues to 436 where the revisedfrequency value 104 is instead given a frequency value higher than thefrequency value of the frequency object 104 associated with the defaultword object 108. After storage of the variant word object 108 and therevised frequency object 104 in the frequency learning database 96,processing continues to 420 where the learning process is terminated,and processing then returns to the main process, as at 204.

With further regard to the learning function, it is noted that thelearning function additionally detects whether both the default wordobject 108 and the variant word object 104 were obtained from thefrequency learning database 96. In this regard, when word objects 108are identified, as at 224, for correspondence with generated prefixobjects, all of the data sources in the memory are polled for suchcorresponding word objects 108 and corresponding frequency objects 104.Since the frequency learning database 96 stores word objects 108 thatalso are stored either in the generic word list 88 or the new wordsdatabase 92, the word object 108 and the associated frequency object 104that are obtained from the frequency learning database 96 typically areduplicates of word objects 108 that have already been obtained from thegeneric word list 88 or the new words database 92. However, theassociated frequency object 104 obtained from the frequency learningdatabase 96 typically has a frequency value that is of a greatermagnitude than that of the associated frequency object 104 that had beenobtained from the generic word list 88. This reflects the nature of thefrequency learning database 96 as imparting to a frequently used wordobject 108 a relatively greater frequency value than it otherwise wouldhave in the generic word list 88.

It thus can be seen that the learning function indicated in FIGS. 5A and5B and described above is generally not initiated until a delimiterinput is detected, meaning that learning occurs only once for eachsession. Additionally, if the final default output is not a user-definednew word, the word objects 108 that are the subject of the frequencylearning function are the word objects 108 which were associated withthe default output 76 and the selected variant output 80 at the timewhen the selection occurred, rather than necessarily being related tothe object that ultimately resulted as the default output at the end ofthe session. Also, if numerous learnable events occurred during a singlesession, the frequency learning function operates only on the wordobjects 108 that were associated with the final learnable event, i.e., aselection event, an alternation event, or a movement event, prior totermination of the current session.

With further regard to the identification of various word objects 108for correspondence with generated prefix objects, it is noted that thememory 20 can include a number of additional data sources 99 in additionto the generic word list 88, the new words database 92, and thefrequency learning database 96, all of which can be consideredlinguistic sources. An exemplary two other data sources 99 are depictedin FIG. 2A, it being understood that the memory 20 might include anynumber of other data sources 99. The other data sources 99 mightinclude, for example, an address database, a speed-text database, or anyother data source without limitation. An exemplary speed-text databasemight include, for example, sets of words or expressions or other datathat are each associated with, for example, a character string that maybe abbreviated. For example, a speed-text database might associate thestring “br” with the set of words “Best Regards”, with the intentionthat a user can type the string “br” and receive the output “BestRegards”.

In seeking to identify word objects 108 that correspond with a givenprefix object, the handheld electronic device 4 may poll all of the datasources in the memory 20. For instance the handheld electronic device 4may poll the generic word list 88, the new words database 92, thefrequency learning database 96, and the other data sources 99 toidentify word objects 108 that correspond with the prefix object. Thecontents of the other data sources 99 may be treated as word objects108, and the processor 16 may generate frequency objects 104 that willbe associated such word objects 108 and to which may be assigned afrequency value in, for example, the upper one-third or one-fourth ofthe aforementioned frequency range. Assuming that the assigned frequencyvalue is sufficiently high, the string “br”, for example, wouldtypically be output to the display 60. If a delimiter input is detectedwith respect to the portion of the output having the association withthe word object 108 in the speed-text database, for instance “br”, theuser would receive the output “Best Regards”, it being understood thatthe user might also have entered a selection input as to the exemplarystring “br”.

The contents of any of the other data sources 99 may be treated as wordobjects 108 and may be associated with generated frequency objects 104having the assigned frequency value in the aforementioned upper portionof the frequency range. After such word objects 108 are identified, thenew word learning function can, if appropriate, act upon such wordobjects 108 in the fashion set forth above.

Again regarding FIG. 3A, when processing proceeds to the filtrationstep, as at 232, and the duplicate word objects 108 and the associatedfrequency objects 104 having relatively lower frequency values arefiltered, the remaining results may include a variant word object 108and a default word object 108, both of which were obtained from thefrequency learning database 96. In such a situation, it can beenvisioned that if a user repetitively and alternately uses one wordthen the other word, over time the frequency objects 104 associated withsuch words will increase well beyond the aforementioned maximum absolutefrequency value for a frequency object 104. Accordingly, if it isdetermined that both the default word object 108 and the variant wordobject 108 in the learning function were obtained from the frequencylearning database 96, instead of storing the variant word object 108 inthe frequency learning database 96 and associating it with a frequencyobject 104 having a relatively increased frequency value, instead thelearning function stores the default word object 108 and associates itwith a revised frequency object 104 having a frequency value that isrelatively lower than that of the frequency object 104 that isassociated with the variant word object 108. Such a schemeadvantageously avoids excessive and unnecessary increases in frequencyvalue.

If it is determined, such as at 268, that the current input is amovement input, such as would be employed when a user is seeking to editan object, either a completed word or a prefix object within the currentsession, the caret 84 is moved, as at 272, to the desired location, andthe flag is set, as at 276. Processing then returns to where additionalinputs can be detected, as at 204.

In this regard, it is understood that various types of movement inputscan be detected from the input device 8. For instance, a rotation of thethumbwheel 32, such as is indicated by the arrow 34 of FIG. 1, couldprovide a movement input, as could the actuation of the <NEXT> key 40,or other such input, potentially in combination with other devices inthe input apparatus 8. In the instance where such a movement input isdetected, such as in the circumstance of an editing input, the movementinput is additionally detected as a selection input. Accordingly, and asis the case with a selection input such as is detected at 252, theselected variant is effectively locked with respect to the defaultportion 76 of the output 64. Any default output 76 during the samesession will necessarily include the previously selected variant.

In the context of editing, however, the particular displayed object thatis being edited is effectively locked except as to the character that isbeing edited. In this regard, therefore, the other characters of theobject being edited, i.e., the characters that are not being edited, aremaintained and are employed as a context for identifying additional wordobjects 108 and the like that correspond with the object being edited.Were this not the case, a user seeking to edit a letter in the middle ofa word otherwise likely would see as a new output 64 numerous objectsthat bear little or no resemblance to the characters of the object beingedited since, in the absence of maintaining such context, an entirelynew set of prefix objects including all of the permutations of thecharacters of the various keystrokes of the object being edited wouldhave been generated. New word objects 108 would have been identified ascorresponding with the new prefix objects, all of which couldsignificantly change the output 64 merely upon the editing of a singlecharacter. By maintaining the other characters currently in the objectbeing edited, and employing such other characters as contextinformation, the user can much more easily edit a word that is depictedon the display 60.

In the present exemplary embodiment of the handheld electronic device 4,if it is determined, as at 252, that the input is not a selection input,and it is determined, as at 260, that the input is not a delimiterinput, and it is further determined, as at 268, that the input is not amovement input, in the current exemplary embodiment of the handheldelectronic device 4 the only remaining operational input generally is adetection of the <DELETE> key 86 of the keys 28 of the keypad 24. Upondetection of the <DELETE> key 86, the final character of the defaultoutput is deleted, as at 280. At this point, the processing generallywaits until another input is detected, as at 284. It is then determined,as at 288, whether the new input detected at 284 is the same as the mostrecent input that was related to the final character that had just beendeleted at 280. If so, the default output 76 is the same as the previousdefault output, except that the last character is the opposite characterof the key actuation that generated the last character. Processing thencontinues to 292 where learning data, i.e., the word object 108 and theassociate frequency object 104 associated with the previous defaultoutput 76, as well as the word object 108 and the associate frequencyobject 104 associated with the new default output 76, are stored in thetemporary learning data register and the flag is set. Such a keysequence, i.e., an input, the <DELETE> key 86, and the same input asbefore, is an alternation input. Such an alternation input replaces thedefault final character with an opposite final character of the key 28which generated the final character 48 of the default output 76. Thealternation input is treated as a selection input for purposes oflocking the default output 76 for the current session, and also triggersthe flag which will initiate the learning function upon detection of adelimiter input at 260.

If it turns out, however, that the system detects at 288 that the newinput detected at 284 is different than the input immediately prior todetection of the <DELETE> key 86, processing continues at 212 where theinput is determined to be either an operational input or an input of akey having one or more characters 48, and processing continuesthereafter.

It is also noted that when the main process reaches the output stage at248, an additional process is initiated which determines whether thevariant component 72 of the output 64 should be initiated. Processing ofthe additional function is initiated from 250 at element 504 of FIG. 6.Initially, the method at 508 outputs the text component 68 of the output64 to the display 60. Further processing determines whether or not thevariant component 72 should be displayed.

Specifically, it is determined, as at 512, whether the variant component72 has already been displayed during the current session. If the variantcomponent 72 has already been displayed, processing continues at 516where the new variant component 72 resulting from the currentdisambiguation cycle within the current session is displayed. Processingthen returns to a termination point at 520, after which processingreturns to the main process at 204. If, however, it is determined at 512that the variant component 72 has not yet been displayed during thecurrent session, processing continues, as at 524, to determine whetherthe elapsed time between the current input and the immediately previousinput is longer than a predetermined duration. If it is longer, thenprocessing continues at 516 where the variant component 72 is displayedand processing returns, through 520, to the main process, as at 204.However, if it is determined at 524 that the elapsed time between thecurrent input and the immediately previous input is less than thepredetermined duration, the variant component 72 is not displayed, andprocessing returns to the termination point at 520, after whichprocessing returns to the main process, as at 204.

Advantageously, therefore, if a user is entering keystrokes relativelyquickly, the variant component 72 will not be output to the display 60,where it otherwise would likely create a visual distraction to a userseeking to enter keystrokes quickly. If at any time during a givensession the variant component 72 is output to the display 60, such as ifthe time between successive inputs exceeds the predetermined duration,the variant component 72 will continue to be displayed throughout thatsession. However, upon the initiation of a new session, the variantcomponent 72 will be withheld from the display if the user consistentlyis entering keystrokes relatively quickly.

An exemplary input sequence is depicted in FIGS. 1 and 7-11. In thisexample, the user is attempting to enter the word “APPLOADER”, and thisword presently is not stored in the memory 20. In FIG. 1 the user hasalready typed the “AS” key 28. Since the data tables in the memory 20are organized according to two-letter prefixes, the contents of theoutput 64 upon the first keystroke are obtained from the N-gram objects112 within the memory. The first keystroke “AS” corresponds with a firstN-gram object 112 “S” and an associated frequency object 104, as well asanother N-gram object 112 “A” and an associated frequency object 104.While the frequency object 104 associated with “S” has a frequency valuegreater than that of the frequency object 104 associated with “A”, it isnoted that “A” is itself a complete word. A complete word is alwaysprovided as the default output 76 in favor of other prefix objects thatdo not match complete words, regardless of associated frequency value.As such, in FIG. 1, the default portion 76 of the output 64 is “A”.

In FIG. 7, the user has additionally entered the “OP” key 28. Thevariants are depicted in FIG. 7. Since the prefix object “SO” is also aword, it is provided as the default output 76. In FIG. 8, the user hasagain entered the “OP” key 28 and has also entered the “L” key 28. It isnoted that the exemplary “L” key 28 depicted herein includes only thesingle character 48 “L”.

It is assumed in the instant example that no operational inputs havethus far been detected. The default output 76 is “APPL”, such as wouldcorrespond with the word “APPLE”. The prefix “APPL” is depicted both inthe text component 68, as well as in the default portion 76 of thevariant component 72. Variant prefix objects in the variant portion 80include “APOL”, such as would correspond with the word “APOLOGIZE”, andthe prefix “SPOL”, such as would correspond with the word “SPOLIATION”.

It is particularly noted that the additional variants “AOOL”, “AOPL”,“SOPL”, and “SOOL” are also depicted as variants 80 in the variantcomponent 72. Since no word object 108 corresponds with these prefixobjects, the prefix objects are considered to be orphan prefix objectsfor which a corresponding word object 108 was not identified. In thisregard, it may be desirable for the variant component 72 to include aspecific quantity of entries, and in the case of the instant exemplaryembodiment the quantity is seven entries. Upon obtaining the result at224, if the quantity of prefix objects in the result is fewer than thepredetermined quantity, the disambiguation function will seek to provideadditional outputs until the predetermined number of outputs areprovided. In the absence of artificial variants having been created, theadditional variant entries are provided by orphan prefix objects. It isnoted, however, that if artificial variants had been generated, theylikely would have occupied a place of preference in favor of such orphanprefix objects, and possibly also in favor of the prefix objects of theresult.

It is further noted that such orphan prefix objects may actually beoffspring orphan prefix objects from suspended parent orphan prefixobjects and/or artificial variants. Such offspring orphan prefix objectscan be again output depending upon frequency ranking as explained below,or as otherwise ranked.

The orphan prefix objects are ranked in order of descending frequencywith the use of the N-gram objects 112 and the associated frequencyobjects 104. Since the orphan prefix objects do not have a correspondingword object 108 with an associated frequency object 104, the frequencyobjects 104 associated with the various N-gram objects 112 must beemployed as a fallback.

Using the N-gram objects 112, the disambiguation function first seeks todetermine if any N-gram object 112 having, for instance, threecharacters is a match for, for instance, a final three characters of anyorphan prefix object. The example of three characters is given since theexemplary embodiment of the handheld electronic device 4 includes N-gramobjects 112 that are an exemplary maximum of the three characters inlength, but it is understood that if the memory 20 included N-gramobjects four characters in length or longer, the disambiguation functiontypically would first seek to determine whether an N-gram object havingthe greatest length in the memory 20 matches the same quantity ofcharacters at the end of an orphan prefix object.

If only one prefix object corresponds in such a fashion to a threecharacter N-gram object 112, such orphan prefix object is listed firstamong the various orphan prefix objects in the variant output 80. Ifadditional orphan prefix objects are matched to N-gram objects 112having three characters, then the frequency objects 104 associated withsuch identified N-gram objects 112 are analyzed, and the matched orphanprefix objects are ranked amongst themselves in order of decreasingfrequency.

If it is determined that a match cannot be obtained with an N-gramobject 112 having three characters, then two-character N-gram objects112 are employed. Since the memory 20 includes all permutations oftwo-character N-gram objects 112, a last two characters of each orphanprefix object can be matched to a corresponding two-character N-gramobject 112. After such matches are achieved, the frequency objects 104associated with such identified N-gram objects 112 are analyzed, and theorphan prefix objects are ranked amongst themselves in descending orderof frequency value of the frequency objects 104 that were associatedwith the identified N-gram objects 112. It is further noted thatartificial variants can similarly be rank ordered amongst themselvesusing the N-gram objects 112 and the associated frequency objects 104.

In FIG. 9 the user has additionally entered the “OP” key 28. In thiscircumstance, and as can be seen in FIG. 9, the default portion 76 ofthe output 64 has become the prefix object “APOLO” such as wouldcorrespond with the word “APOLOGIZE”, whereas immediately prior to thecurrent input the default portion 76 of the output 64 of FIG. 8 was“APPL” such as would correspond with the word “APPLE.” Again, assumingthat no operational inputs had been detected, the default prefix objectin FIG. 9 does not correspond with the previous default prefix object ofFIG. 8. As such, the first artificial variant “APOLP” is generated andin the current example is given a preferred position. The aforementionedartificial variant “APOLP” is generated by deleting the final characterof the default prefix object “APOLO” and by supplying in its place anopposite character 48 of the key 28 which generated the final characterof the default portion 76 of the output 64, which in the current exampleof FIG. 9 is “P”, so that the aforementioned artificial variants is“APOLP”.

Furthermore, since the previous default output “APPL” corresponded witha word object 108, such as the word object 108 corresponding with theword “APPLE”, and since with the addition of the current input theprevious default output “APPL” no longer corresponds with a word object108, two additional artificial variants are generated. One artificialvariant is “APPLP” and the other artificial variant is “APPLO”, andthese correspond with the previous default output “APPL” plus thecharacters 48 of the key 28 that was actuated to generate the currentinput. These artificial variants are similarly output as part of thevariant portion 80 of the output 64.

As can be seen in FIG. 9, the default portion 76 of the output 64“APOLO” no longer seems to match what would be needed as a prefix for“APPLOADER”, and the user likely anticipates that the desired word“APPLOADER” is not already stored in the memory 20. As such, the userprovides a selection input, such as by scrolling with the thumbwheel 32,or by actuating the <NEXT> key 40, until the variant string “APPLO” ishighlighted. The user then continues typing and enters the “AS” key.

The output 64 of such action is depicted in FIG. 10. Here, the string“APPLOA” is the default portion 76 of the output 64. Since the variantstring “APPLO” became the default portion 76 of the output 64 (notexpressly depicted herein) as a result of the selection input as to thevariant string “APPLO”, and since the variant string “APPLO” does notcorrespond with a word object 108, the character strings “APPLOA” and“APPLOS” were created as an artificial variants. Additionally, since theprevious default of FIG. 9, “APOLO” previously had corresponded with aword object 108, but now is no longer in correspondence with the defaultportion 76 of the output 64 of FIG. 10, the additional artificialvariants of “APOLOA” and “APOLOS” were also generated. Such artificialvariants are given a preferred position in favor of the three displayedorphan prefix objects.

Since the current input sequence in the example no longer correspondswith any word object 108, the portions of the method related toattempting to find corresponding word objects 108 are not executed withfurther inputs for the current session. That is, since no word object108 corresponds with the current input sequence, further inputs willlikewise not correspond with any word object 108. Avoiding the search ofthe memory 20 for such nonexistent word objects 108 saves time andavoids wasted processing effort.

As the user continues to type, the user ultimately will successfullyenter the word “APPLOADER” and will enter a delimiter input. Upondetection of the delimiter input after the entry of “APPLOADER”, thelearning function is initiated. Since the word “APPLOADER” does notcorrespond with a word object 108 in the memory 20, a new word object108 corresponding with “APPLOADER” is generated and is stored in the newwords database 92, along with a corresponding new frequency object 104which is given an absolute frequency in the upper, say, one-third orone-fourth of the possible frequency range. In this regard, it is notedthat the new words database 92 and the frequency learning database 96are generally organized in two-character prefix data tables similar tothose found in the generic word list 88. As such, the new frequencyobject 104 is initially assigned an absolute frequency value, but uponstorage the absolute frequency value, if it is not the maximum valuewithin that data table, will be changed to include a normalizedfrequency value percentage normalized to whatever is the maximumfrequency value within that data table.

As a subsequent example, in FIG. 11 the user is trying to enter the word“APOLOGIZE”. The user has entered the key sequence “AS” “OP” “OP” “L”“OP”. Since “APPLOADER” has now been added as a word object 108 to thenew words database 92 and has been associated with frequency object 104having a relatively high frequency value, the prefix object “APPLO”which corresponds with “APPLOADER” has been displayed as the defaultportion 76 of the output 64 in favor of the variant prefix object“APOLO”, which corresponds with the desired word “APOLOGIZE.” Since theword “APOLOGIZE” corresponds with a word object 108 that is stored atleast in the generic word list 88, the user can simply continue to enterkeystrokes corresponding with the additional letters “GIZE”, which wouldbe the letters in the word “APOLOGIZE” following the prefix object“APOLO”, in order to obtain the word “APOLOGIZE”. Alternatively, theuser may, upon seeing the output 64 depicted in FIG. 11, enter aselection input to affirmatively select the variant prefix object“APOLO”. In such a circumstance, the learning function will be triggeredupon detection of a delimiter symbol, and the word object 108 that hadcorresponded with the character string “APOLO” at the time the selectioninput was made will be stored in the frequency learning database 96 andwill be associated with a revised frequency object 104 having arelatively higher frequency value that is similarly stored in thefrequency learning database 96.

An additional feature of the handheld electronic device 4 is depictedgenerally in FIG. 12. In some circumstances, it is desirable that thedisambiguation function be disabled. For instance, when it is desired toenter a password, disambiguation typically is relatively more cumbersomethan during ordinary text entry. As such, when the system focus is onthe component corresponding with the password field, the componentindicates to the API that special processing is requested, and the APIdisables the disambiguation function and instead enables, for instance,a multi-tap input interpretation system. Alternatively, other inputinterpretation systems could include a chording system or apress-and-hold/press-and-release interpretation system. As such, whilean input entered with the disambiguation function active is an ambiguousinput, by enabling the alternative interpretation system, such as theexemplary multi-tap system, each input can be largely unambiguous.

As can be understood from FIG. 12, each unambiguous input is displayedfor a very short period of time within the password field 120, and isthen replaced with another output, such as the asterisk. The character“R” is shown displayed, it being understood that such display is onlyfor a very short period of time.

As can be seen in FIGS. 1 and 7-11, the output 64 includes the displayedgraphic 46 near the lower end of the variant component 72, and that thedisplayed graphic 46 is highly similar to the graphic 42 of the <NEXT>key 40. Such a depiction provides an indication to the user which of thekeys 28 of the keypad 24 can be actuated to select a variant output. Thedepiction of the displayed graphic 46 provides an association betweenthe output 64 and the <NEXT> key 40 in the user's mind. Additionally, ifthe user employs the <NEXT> key 40 to provide a selection input, theuser will be able to actuate the <NEXT> key 40 without moving the user'shands away from the position the hands were in with respect to thehousing 6 during text entry, which reduces unnecessary hand motions,such as would be required if a user needed to move a hand to actuate thethumbwheel 32. This saves time and effort.

It is also noted that the system can detect the existence of certainpredefined symbols as being delimiter signals if no word object 108corresponds with the text entry that includes the symbol. For instance,if the user desired to enter the input “one-off”, the user might beginby entering the key sequence “OP” “BN” “ER” “ZX” “OP”, with the “ZX”actuation being intended to refer to the hyphen symbol disposed thereon.Alternatively, instead of typing the “ZX” key the user might actuate an<ALT> entry to unambiguously indicate the hyphen.

Assuming that the memory 20 does not already include a word object 108of “one-off”, the disambiguation function will detect the hyphen asbeing a delimiter input. As such, the key entries preceding thedelimiter input will be delimited from the key entries subsequent to thedelimiter input. As such, the desired input will be searched as twoseparate words, i.e., “ONE” and “OFF”, with the hyphen therebetween.This facilitates processing by more narrowly identifying what is desiredto be searched.

The handheld electronic device 4 can also be configured to identify andprovide proposed compound language solutions to an ambiguous input. Forinstance, a user may seek to input the word “highschool”, which can besaid to be a compound language expression that comprises the words“high” and “school”. If it is assumed that the word “highschool” is notalready stored as a language object 100 in the memory 20, the handheldelectronic device 4 can encounter difficulty when attempting todisambiguate such an ambiguous input. Advantageously, therefore, thehandheld electronic device 4 is configured to seek compound languagesolutions in certain circumstances.

As a general matter, the handheld electronic device 4 will seek toidentify and output at a position of relatively higher priority, i.e.,at the top of a list, one or more proposed outputs that arerepresentative of at least a portion of a language object 100 thatcorresponds with an ambiguous input in its entirety. That is, singleword solutions are considered to be preferred over compound languagesolutions. However, compound language solutions can be identified andoutput as being solutions that are relatively less preferred than singleword solutions but that are more preferred than solutions that includeartificial variants. By way of example, therefore, the handheldelectronic device 4 can, in response to an ambiguous input, provide anoutput that comprises a plurality of solutions, with a number of thesolutions corresponding with single word solutions and being output at aposition of highest priority, with a number of compound languagesolutions output at a position of relatively moderate priority, and witha number of solutions based upon artificial variants that are output ata position of relatively low priority. The quantity of results can betailored based upon user preference, and thus may include fewer than allof the results mentioned above.

It is noted that a compound language solution typically isrepresentative of two or more language objects 100, meaning thatcompound language solutions can be representative of a pair of languageobjects 100 and/or can be representative of three or more languageobjects 100. For the sake of simplicity in illustrating some of theaspects of the disclosed and claimed concept, a first set of examplespresented below are described in terms of compound language solutionsthat are representative of two language objects 100. As will be setforth in greater detail below, however, the same aspects exist in andcan be obtained from compound language solutions that are representativeof three or more language objects 100.

As is depicted generally in FIGS. 13-13D, an exemplary ambiguous input607 (FIG. 13) is shown as including seven input member actuationsrepresented by the encircled digits 1 through 7. The disambiguationroutine 22 will first seek to identify one or more language objects 100that correspond with the ambiguous input in its entirety. That is, thedisambiguation routine 22 will seek to identify language objects 100having seven or more linguistic elements and that correspond with theentire ambiguous input 607. Depending upon the ambiguous input 607, itis possible that no such corresponding language object 100 can beidentified in the memory 20.

Depending upon the ambiguous input 607, the disambiguation routine 22may additionally seek to interpret the ambiguous input 607 as a compoundlanguage input. In the depicted exemplary embodiment, the disambiguationroutine 22 seeks compound language solutions whenever a language object100 is identified that corresponds with a first portion of the ambiguousinput 607 and that has a length that is equal to the length as the firstportion. As employed herein, the expression “length” and variationsthereof shall refer broadly to a quantity of elements of which an objectis comprised, such as the quantity of linguistic elements of which alanguage object 100 is comprised.

The disambiguation routine 22 seeks compound language solutions inresponse to an ambiguous input if an initial portion of the ambiguousinput is determined to be the same as a language object 100 in thememory 20. In the example presented herein, such an “initial portion”begins with the first input member actuation of the ambiguous input 607and ends prior to the final input member actuation, although variationsare possible.

For instance, if it is assumed that a user in inputting the ambiguousinput 607 is seeking to input the word “highschool”, the disambiguationroutine 22 would already have recognized at various points during entryof the ambiguous input 607 that various initial portions of theambiguous input 607 corresponded with various language objects 100 andhad a length equal thereto. As is depicted generally in FIG. 13A, duringentry of the ambiguous input 607, the disambiguation routine 22 wouldhave recognized that the first two input member actuations, namely <GH>and <UI>, i.e., a first portion 611A, were an initial portion thatcorresponded with and were of an equal length to the length of thelanguage object 100 for “hi”. Such recognition would have occurred withthe second input member actuation.

With the first portion 611A having been identified as representing acomplete word as represented by a language object 100, thedisambiguation routine 22 would thus seek to identify another languageobject 100 that corresponded with another portion of the ambiguous input607 successive to the first portion 611A. It is reiterated that some ofthe examples presented herein are described in terms of two-componentcompound language solutions for the sake of simplicity, and in thepresent example, therefore, the disambiguation routine 22 would seek toidentify a language object 100 that corresponded with a second portion615A of the ambiguous input 607. Such second portion 615A would compriseactuations of the keys 28 <GH> <GH> <AS> <CV> and <GH> following thefirst portion 611A. If it is assumed that no language object 100 can befound in the memory 20 that corresponds with such second portion 615A,the two-component compound language solution sought in the fashiondepicted generally in FIG. 13A would fail. It is stated for purposes ofcompleteness that one or more compound language solutions representativeof three or more language objects 100 potentially could be found for theambiguous input 607 but are not illustrated herein.

The disambiguation routine 22 would additionally have noted that thefirst three input member actuations, i.e., <GH> <UI> <GH>, are anotherfirst portion 611B of the ambiguous input 607 that corresponds with andhas a length equal to that of a language object 100 in the memory 20,specifically, the language object 100 for the word “hug”, as is depictedgenerally in FIG. 13B. The disambiguation routine 22 thus will seek toidentify a language object 100 in the memory 20 that corresponds with asecond portion 615B of the ambiguous input 607, i.e., <GH> <AS> <CV><GH>. If it is assumed that a language object 100 in the memory 20exists for the English word “hachure”, the disambiguation routine willinterpret the ambiguous input 607 as potentially being an attemptedinput of the compound language expression “hughachure”. That is, apotential compound language solution for the ambiguous input 607 wouldbe representative of the language object 100 for “hug” and the languageobject 100 for “hachure”.

As will be described in greater detail below, and as is depicted in FIG.14, the handheld electronic device 4 can output “hughhach” as arepresentation 619B of the compound language solution “hughachure”, withsuch representation 619B comprising a representation of the languageobject 100 for “hug” and a representation of a portion of the languageobject 100 for “hachure”. This representation 619B is also depictedschematically in FIG. 13B.

Similarly, and as depicted generally in FIG. 13C, the disambiguationroutine 22 will have recognized that the first four input memberactuations of the ambiguous input 607 likewise are a first portion 611Cof the ambiguous input 607 that corresponds with and has a length equalto that of a language object 100 in the memory 20, specifically, for theword “high”. The disambiguation routine 22 will thus seek to identify alanguage object 100 in the memory 20 that corresponds with a secondportion 615C of the ambiguous input 607 that follows the first portion611C. Specifically, the disambiguation routine 22 will determine thatthe second portion 615C, i.e., <AS> <CV> <GH>, corresponds with thelanguage object 100 for the word “school”. The disambiguation routine 22thus may additionally determine that the ambiguous input 607 may be anattempt by the user to input the compound expression “highschool”, thusgenerating the compound language solution “high” plus “school”. Thedevice could output “highsch” as a representation 619C of such compoundlanguage solution. The representation 619C is depicted schematically inFIG. 13C.

Furthermore, and as depicted generally in FIG. 13D, the disambiguationroutine 22 may determine that the first five input member actuationsconstitute another first portion 611D that corresponds with and has alength equal to that of the language object 100 for the word “highs”.The disambiguation routine will thus also seek to identify a secondlanguage object 100 that corresponds with a second portion 615D of theambiguous input 607 that follows the first portion 611D. For instance,the disambiguation routine might identify the language object 100 forthe word “choice” as corresponding with the second portion 615D. Thedisambiguation routine 22 thus would interpret the ambiguous input 607as potentially being an attempt by the user to enter the compoundlanguage expression “highschoice” by generating the compound languagesolution “highs” plus “choice”. The handheld electronic device 4 couldoutput “highsch” as a representation 619D of the compound languagesolution “highs” plus “choice”, it being noted that this representation619D is the same as the representation 619B, with the representations619B and 619D thus being output as a single variant to avoid undesirableduplication.

In order to limit the generation of compound language solutions having avery low likelihood or no likelihood of being what a user intended toenter, the disambiguation routine 22 additionally performs an analysisof the combination of the language objects 100 making up a compoundlanguage solution. Specifically, and as is depicted generally in FIGS.13B, 13C, and 13D, at least one junction object 639B, 639C, and 639D isgenerated for each compound language solution. In the example depictedin FIGS. 13B, 13C, and 13D, the junction object of a compound solutionis the terminal linguistic element of the one language object followedby the initial linguistic element of the adjacent language object. Thus,the junction object 639B of the compound language solution “hug” plus“hachure” is the linguistic element string “gh”. Similarly, the junctionobject 639C of the compound language solution “high” plus “school” isthe linguistic element string “hs”. Still similarly, the junction object639D of the compound language solution “highs” plus “choice” is thelinguistic element string “sc”.

Each junction object 639B, 639C, and 639D, etc., is sought to becompared with one or more N-gram objects 112 in the memory 20. Thisprovides the disambiguation routine 22 an opportunity to takeappropriate action if the junction object 639B, 639C, and 639D, etc., isof a very low frequency or is nonexistent in the present language. Inthe present example, the junction objects 639B, 639C, and 639D are eachtwo linguistic elements in length and thus would each be compared with anumber of the 2-gram objects.

If a particular junction object corresponds with an N-gram object 112that is associated with a frequency object 104 having a relatively lowfrequency value, such as a frequency value below a predeterminedthreshold, this would indicate that the particular compound languagesolution from which the particular junction object was derived isextremely unlikely to be the entry desired by the user. That is, sincethe frequency value of a frequency object 104 associated with an N-gramobjects 112 indicates the relative probability that the character stringrepresented by that particular N-gram object 112 exists at any locationwithin any word of the relevant language, the correspondence of alow-probability N-gram 112 with a junction object indicates of alow-probability compound language solution.

Similarly, if no N-gram object 112 can be found that corresponds with aparticular junction object, this would also indicate a low probabilityor a zero probability compound language solution. In the presentexemplary embodiment, the memory 20 has stored therein all of thetwo-character permutations of the twenty-six Latin letters. As such, inthe present exemplary configuration a 2-gram object will generallyalways be identified as corresponding with a junction object have alength of two linguistic elements. As will be explained below, however,a junction object can be more than two linguistic elements in length,and the exemplary embodiment of the handheld electronic device 4 has fewthan all of the three-character permutation of the twenty-six Latinletters. In some circumstances, therefore, it is possible that an N-gramobject 112 cannot be found that corresponds with a particular junctionobject. It is also noted that in other embodiments the handheldelectronic device may have fewer than all of the two-characterpermutations of the twenty-six Latin letters stored therein, and a lackof N-gram correspondence with a junction object could occur in thissituation as well.

In the present exemplary embodiment, the disambiguation routine 22assigns to a given compound language solution the frequency value of theN-gram object 112 identified as corresponding with the junction objectof the compound language solution. If no corresponding N-gram object 112was identified, a frequency value of zero is assigned. Thedisambiguation routine 22 can arrange the compound language solutions indecreasing order of frequency value. If the frequency value of acompound language solution is zero or is below a predeterminedthreshold, the disambiguation routine can, for example, suppress thecompound language solution from the output or can output it at aposition of relatively low probability.

In the present example, the linguistic element string “gh”, “hs”, and“sc” of the junction objects 639B, 639C, and 639D are unlikely to be ofan undesirably low probability, and the corresponding compound languagesolutions are thus unlikely to suppressed from the output. On the otherhand, a junction object in the form of the linguistic string “qg” likelywould result in the corresponding compound language solution beingsuppressed or at least output at a position of relatively low priority.

In order to limit the generation of compound language solutions having avery low likelihood of being what a user intended to enter, thedisambiguation routine 22 additionally limits the data sources fromwhich second and subsequent language objects 100 of a compound languagesolution can be identified. For instance, the generic word list 88 is adata source that is substantially inviolate and has stored thereinvarious language objects 100. The generic word list 88 can be the sourceof any of the linguistic objects 100 of which a compound languagesolution is comprised. On the other hand, the new words database 92, forexample, has stored therein language objects 100 representative ofcustom words, and the contents of the new words database 92 can change.While the new words database 92 can be the source a first languageobject 100 of a compound language solution, the new words database 92will not, in the present exemplary embodiment, be a source of a secondor subsequent language object 100 of a compound language solution.

It is further noted that an alphabet on the handheld electronic device 4comprises all of the linguistic elements that are available on thehandheld electronic device 4. The alphabet comprises a core alphabet andan extended alphabet. In the present exemplary embodiment, the corealphabet is comprised of the twenty-six Latin letters. The languageobjects in the generic word list are comprised only of the linguisticelements of the core alphabet. The extended alphabet compriseslinguistic elements other than the twenty-six Latin letters. Thelinguistic element in the extended alphabet thus might includecharacters in non-Latin languages, and may additionally or alternativelyinclude Latin letters with diacritics such as the Latin letter “U” withan umlaut, thus “Ü”. In this regard, the new words database 92 mightinclude a language object 100 representative of the word “MÜNCHEN” andanother language object 100 representative of the word “ÜBER”.

In disambiguating an ambiguous input, the disambiguation routine looks,at least initially, in all of the data sources on the handheldelectronic device 4 to identify language objects 100 that correspondwith the ambiguous input. If it is determined, however, that no singlelanguage object 100 corresponds with the entire ambiguous input, butthat an initial portion of the ambiguous input corresponded with and wasof an equal length to a language object 100, the disambiguation routine22 looks only in the generic word list 88, i.e., a static data source,for language objects 100 that might correspond with portions of theambiguous input succeeding the initial portion thereof. As such, itwould be possible, depending upon the ambiguous input, for the handheldelectronic device 4 to identify “ÜBERGENIUS” as a compound languagesolution. That is, the language object 100 for “ÜBER” could have beenidentified in the new words database 92, and the language object 100 for“GENIUS” could have been identified in the generic word list 88. Itwould not, however, be possible for it to identify “GOLDMÜNCHEN” as acompound language solution when a user has actuated the keys <GH> <OP><L> <DF> <M> <UI> during an intended entry of the word “GOLDMINE”. Thatis, while the language object 100 for “GOLD” could have been identifiedin the generic word list 88, the language object 100 for “MÜNCHEN” wouldnot have been identified as a second or subsequent word of such acompound language solution since only the generic word list 88 isexamined in seeking such second or subsequent words, and the languageobject 100 for “MÜNCHEN” in the present example is stored in the newwords database 92.

As a general matter, in response to an ambiguous input, any generatedcompound language solutions are output at a position of relatively lowerpriority than any language object 100 that corresponds with the entireambiguous input. The compound language solutions are themselves arrangedand output in decreasing order of priority according to the increasingquantity of language objects 100 therein. That is, a compound languagesolution comprised of two language objects 100 will be output at aposition of relatively higher priority than a compound language solutioncomprised of three language objects 100, and so forth.

If a plurality of compound language solutions each are comprised of thesame quantity of language objects 100, such as in the example depictedin FIGS. 13-13D wherein the compound language solutions are eachcomprised of two language objects 100, a length identity value iscalculated for each such compound language solution. The length identitycalculation depends upon whether the compound language solution iscomprised of two language objects 100 or is comprised of three or morelanguage objects 100.

If a compound language solution is comprised of two language objects100, the ambiguous input can thus can be said to include a first portionand a second portion. The difference in length between the first portionand the second portion is determined to be the length identity for thecompound language solution. By way of example, the compound languagesolution of FIG. 13B is of a length identity having a value of 1, thecompound language solution in FIG. 13C would also be of a lengthidentity having a value of 1, and the compound language solution in FIG.13D would be of a length identity having a value of 3.

If the compound language solution is comprised of three or more languageobjects 100, the ambiguous input can be said to comprise three or moreportions. In such a situation, the length identity of the compoundlanguage solution is the sum of each length difference between a givenportion of the ambiguous input having a given length and the portion ofthe ambiguous input having the next greatest length shorter than thegiven length. For instance, the ambiguous input 607 of FIG. 13 may haveresulted in the generation of three compound language solutions eachbeing comprised of three language objects, as set forth in FIGS. 13E,13F, and 13G.

In FIG. 13E, a first portion 611E of the ambiguous input 607 is threelinguistic elements in length, a second portion 615E is two linguisticelements in length, and a third portion 657E is two linguistic elementsin length. The difference in length between the longest portion, i.e.,the first portion 611E three linguistic elements in length, and theportion having the next greatest length, i.e., either of the second andthird portions 615E and 657E each two linguistic elements in length,is 1. There is zero difference in length between the second and thirdportions 615E and 657E. Thus, 1 plus zero equals 1, and the calculatedvalue of the length identity for the compound language solution, arepresentation of a portion of which is depicted schematically at thenumeral 619E, is 1.

It is noted that FIG. 13E depicts a first junction object 639E and asecond junction object 659E generated for the compound language solutionthereof. If either of the junction objects 639E and 659E correspondswith an N-gram 112 associated with a frequency object 104 having afrequency value below a predetermined threshold, or if no correspondingN-gram 112 can be found for either of the junction objects 639E and659E, the compound language solution likely will be suppressed and notbe output.

In FIG. 13F, a first portion 611F of the ambiguous input 607 is threelinguistic elements in length, a second portion 615F is three linguisticelements in length, and a third portion 657F is one linguistic elementin length. There is zero difference in length between the first andsecond portions 611F and 615F, which are each the longest portions. Thedifference in length between either of the longest portions, i.e., thefirst and second portions 611F and 615F which are each three linguisticelements in length, and the portion having the next greatest length,i.e., the third portion 657F one linguistic element in length, is 2.Thus, zero plus 2 equals 2, and the calculated value of the lengthidentity for the compound language solution, a representation of aportion of which is depicted schematically at the numeral 619F, is 2.

In FIG. 13G, a first portion 611G of the ambiguous input 607 is twolinguistic elements in length, a second portion 615G is four linguisticelements in length, and a third portion 657G is one linguistic elementin length. The difference in length between the longest portion, i.e.,the second portion 615F four linguistic elements in length, and theportion having the next greatest length shorter than this length, i.e.,the first portion 611G having a length of two linguistic elements, is 2.The difference in length between the first portion 611F two linguisticelements in length, and the portion having the next greatest lengthshorter than this length, i.e., the third portion 657F one linguisticelement in length, is 1. Thus, 2 plus 1 equals 3, and the calculatedvalue of the length identity for the compound language solution, arepresentation of a portion of which is depicted schematically at thenumeral 619G, is 3.

The plurality of compound language solutions that each are comprised ofthe same quantity of language objects 100 are then output with respectto one another in decreasing order of priority according to theincreasing calculated value of length identity. It is noted that thecompound language solutions can be said to have a progressively lesser“degree” of “length identity” as the calculated length identityincreases in value.

If a plurality of the compound language solutions that are comprised ofthe same quantity of language objects 100 additionally have the samecalculated length identity value, these compound language solutions areassigned a compound frequency value. The compound frequency value of acompound language solution is, in the present example, an average of thefrequency values of the frequency objects 104 associated with thelanguage objects 100 of the compound language solution. Alternatively,the compound frequency value could be defined as the frequency value ofthe final language object 100 of the compound language solution, orstill alternatively could be defined in other appropriate fashions.Regardless of the specific fashion in which a compound frequency valueis determined, such compound language solutions are output with respectto one another in decreasing order of priority according to thedecreasing compound frequency value.

In this regard, it is noted that this compound frequency value isdifferent than the frequency value associated with the compound languagesolution as a result of comparing a junction object thereof with theN-gram objects 112. A compound language solution comprised of three ormore language objects 100 will have a plurality of junction objectsgenerated therefor, for example. If any of the junction objects of anycompound language solution indicates a probability of zero or aprobability below a predetermined threshold, in the present exemplaryembodiment such compound language solution will be unlikely to be outputat all, and thus will not be considered when arranging representationsof compound language solutions in priority order for output.

In the example of FIGS. 13-13D, the disambiguation routine 22 hasdetermined that no language object 100 corresponds with the entireambiguous input 607, but has determined that the ambiguous input 607could represent an attempt by the user to input any of three possiblecompound language expressions, with each compound language expressionbeing comprised of two language objects 100. It is noted that theexample of FIGS. 13E-13G shall be considered no further. Thedisambiguation routine 22 thus will output at least some of the possiblecompound language solutions, as is indicated generally in FIG. 14.

The various compound language solutions of FIGS. 13B-13D, each beingcomprised of two language objects 100, are output in order according tothe algorithm described above. Any solutions resulting from a singlelanguage object 100 corresponding with the entire ambiguous input 607would be output at a position of relatively highest priority in order ofdecreasing frequency value. In the present example, no such singlelanguage objects 100 were found to correspond with the ambiguous input607.

Representations of the various compound language solutions of FIGS.13B-13D, each being comprised of two language objects 100, are outputaccording to a decreasing degree of length identity. That is, asmentioned above, the compound language solutions are output in order ofincreasing calculated value of length identity. In the present example,representations of the compound language solutions of FIGS. 13B and 13Cwould each be output at a position of relatively higher priority thatthe compound language solution of FIG. 13D.

It is noted, however, that the compound language solutions of FIGS. 13Band 13C both have a length identity with a value of 1. The compoundlanguage solutions having the same length identity value will thus beoutput amongst themselves according to decreasing compound frequencyvalue.

For instance, if it is assumed that the frequency value of the languageobject 100 for “hug” is 25,000, and that the frequency value of thelanguage object 100 for “hachure” is 2000, the two frequency valueswould be summed and divided by two to obtain a compound frequency valueof 13,500 for the compound language solution of FIG. 13B. If it isfurther assumed that the frequency value of the language object 100 for“high” is 26,000, and that the frequency value of the language object100 for “school” is 14,000, the two frequency values would be summed anddivided by two to obtain a compound frequency value of 20,000. Since20,000 is greater than 13,500, the compound language solution 619“HIGHSCH” would be output as being of a relatively higher priority thanthe compound language solution 623 “HUGHACH”. It is noted that thecompound language solution for FIG. 13D ordinarily would be output at aposition of relatively lower priority than the compound languagesolution 623 “HUGHACH”, however the compound language solution for FIG.13D would be the same as the compound language solution 619 “HIGHSCH”.The compound language solution for FIG. 13D thus would not be outputinasmuch as it would constitute a duplicate compound language solutionfor ambiguous input 607.

An exemplary flowchart of a method is indicated generally in FIGS.15A-15C. For purposes of clarity, the flowchart of FIGS. 15A-15C isdirected toward the exemplary situation depicted generally in FIGS.13B-13D wherein the three generated compound language solutions are eachcomprised of two language objects 100. As such, the typical first stepin the general analysis set forth herein wherein compound languagesolutions comprised of relatively lesser quantities of language objects100 are placed at positions of relatively higher priority than othercompound language solutions comprised of relatively greater quantitiesof language objects 100 is obviated in the present example.

As mentioned elsewhere herein, it is determined, as at 255 in FIG. 3A,whether any language objects 100 were identified as corresponding withthe ambiguous input. If not, processing branches, as at 226, to FIG. 15Afor a subsystem.

It is then determined, as at 713, whether or not a language object hasalready been identified that corresponds with a first portion of theambiguous input and that has a length equal to the length of such firstportion. If not, processing returns to the main process at 228 where theneed for artificial variants can be determined.

However, if it is determined at 713 that a language object waspreviously identified that corresponds with and has a length equal to afirst portion of the ambiguous input, processing continues, as at 717,where it is determined whether or not a second language objectcorresponds with a second portion of the ambiguous input following thefirst portion. If it is determined at 717 that such a second languageobject has been identified, processing continues, as at 721, where alength identity is determined for the compound language solution andfrequency values for the first and second language objects are obtained.

However, if it is determined at 717 that a second language object cannotbe identified as corresponding with the second portion of the ambiguousinput that follows the first portion, processing continues, as at 725,where it is determined whether or not a suffix portion of the ambiguousinput that follows the first portion of the ambiguous input isconsistent with a suffix object stored in the memory 20. In this regard,it is noted that certain languages are considered to be analyticlanguages, and certain languages are considered to by syntheticlanguages. In an analytic language, compounds are simply elements strungtogether without any addition characters or markers. English, forexample, is an analytic language.

On the other hand, the German compound kapitänspatent consists of thelexemes kapitän and patent joined by the genitive case marker s. In theGerman language, therefore, the genitive case marker s potentially couldbe a suffix object from among a number of predetermined suffix objectsstored in the memory 20.

As such, if it is determined at 717 that no second language objectcorresponds with the second portion of the ambiguous input following thefirst portion of the ambiguous input, processing continues to 725 whereit is determined whether or not a portion of the ambiguous input thatfollows the first portion of the ambiguous input, i.e., a suffixportion, is consistent with a suffix object in the memory 20.

For instance, if an ambiguous input had been <JK> <AS> <OP> <UI> <TY><AS> <BN> <AS> <OP> <AS> <TY> <ER> <BN>, the disambiguation routine 22would have determined at 713 that the first seven input memberactuations, i.e., <JK> <AS> <OP> <UI> <TY> <AS> <BN>, had beenidentified as constituting a first portion of the ambiguous input thatcorresponds with and has a length equal to the language object 100 for“kapitän”. In the present example, it is assumed that the disambiguationroutine 22 would have determined at 717 that no language object 100corresponds with the portion of the ambiguous input that follows such afirst portion, i.e., no language object 100 would exist for <AS> <OP><AS> <TY> <ER> <BN>.

The exemplary disambiguation routine 22 would then determine, as at 725,whether the input member actuation <AS> following the first portion ofthe ambiguous input, i.e., <JK> <AS> <OP> <UI> <TY> <AS> <BN>,constitutes a suffix portion that is consistent with a suffix object inthe memory 20. In the present example, it is assumed that the genitivecase marker s is a suffix object stored in the memory 20. Thedisambiguation routine thus would determine at 725 that the input memberactuation <AS> corresponds with the genitive case marker s, meaning thatthe input member actuation <AS> is consistent with a suffix object inthe memory 20.

If yes, processing then continues, as at 729, where it is determinedwhether or not a language object 100 corresponds with a second portionof the ambiguous input following the identified suffix portion. That is,the disambiguation routine 22 will determine whether or not a languageobject 100 can be found that corresponds with <OP> <AS> <TY> <ER> <BN>.In the present example, the disambiguation routine 22 would determinethat the language object 100 for “patent” corresponds with such a secondportion of the ambiguous input that follows the suffix portion of theambiguous input. If yes, processing continues at 733 where a lengthidentity is determined for the compound language solution, and frequencyvalues are obtained for the frequency objects that are used to obtainthe compound language solution.

Specifically, the length identity for a compound language solution thatincludes a suffix object would be the difference in length between anextended first portion, i.e., the first portion plus the suffix portion,and the second portion. In the present example, the length of kapitänsis eight characters, and the length of paten is five characters. Thus,the length identity for the compound language solution “kapitänspatent”would have a value of 3. The frequency values obtained would be thosefor the language objects 100 for kapitän and for patent.

It may be determined at 725 that no suffix object in the memory 20corresponds with a portion of the ambiguous input that follows the firstportion. It alternatively may be determined at 729 that no languageobject 100 corresponds with a second portion of the ambiguous inputfollowing a suffix portion of the ambiguous input identified at 725. Ineither situation, an attempted compound language solution will fail, andprocessing will proceed to 737.

Once a compound language solution is identified or fails, as describedabove, processing continues, as at 737, where it is determined whetherany other language objects 100 have been identified that correspond witha first portion of the ambiguous input and that have a length equal tothe first portion. In this regard, such other language objects 100 maybe alternative language objects 100 that were identified for the samefirst portion, such as where language objects 100 for “hug” and for“gig” would be first language objects 100 each corresponding with andhaving a length equal to the same first portion 611B of the ambiguousinput 607 of FIG. 13B, i.e., the first three input member actuations.Alternatively, the additional language objects 100 might be otherlanguage objects 100 that correspond with a different first portion ofthe ambiguous input, such as in the way the language object 100 for “hi”corresponded with and had a length equal to a two-character firstportion 611A of the ambiguous input 607, and the language object 100 for“hug” corresponded with and had a length equal to a three-characterfirst portion 611B of the ambiguous input 607. If at 737 it isdetermined that another first language object 100 has been identifiedfor which compound language processing has not yet been performed,processing continues to 717 where, for instance, it is determinedwhether a second language object 100 corresponds with a second portionof the ambiguous input following such first portion of the ambiguousinput for which the another first language object 100 had beenidentified.

If it is determined at 737 that no such other first language objectshave been identified, meaning that all possible compound languagesolutions have been identified, processing continues, as at 741, wherethe compound language solutions are output in order of decreasing degreeof length identity, i.e., in increasing order of the value of the lengthidentity of the various compound language solutions. Pursuant to suchoutput, it is determined, as at 745, whether any compound languagesolutions have an equal length identity. If so, processing continues, asat 749, where the frequency values of the language objects from whichthe compound language solutions were derived are averaged to obtain acompound frequency value for each such compound language solution. Suchcompound language solutions of equal length identity are morespecifically output, as at 753, in order of decreasing frequency valueat the position that corresponds with the length identity of suchcompound language solutions. Processing then continues, as at 701, whereadditional input member actuations of the ambiguous input can bedetected.

It is noted that noted that a suffix portion of an ambiguous input isnot limited to a single input member actuation, and that a plurality ofinput member actuations can be analyzed as a suffix portion to determinewhether such suffix portion is consistent with a predetermined suffixobject in the memory 20. It is further noted that a junction objectgenerated in the context of an identified suffix portion will comprisethe suffix portion in addition to the terminal linguistic element of thepreceding language object and the initial linguistic element of thesucceeding language object. Moreover, it is noted that suffix portionscan be identified and employed in the context of compound languagesolutions comprising three or more language objects 100 and need not belimited to positions immediately succeeding a first language object 100in a compound language solution, it being noted that suffix portions canbe identified and employed successive to second and subsequent languageobjects 100 of a compound language solution.

It is further noted that the disambiguation routine 22 can be employedto identify compound language solutions when the ambiguous inputincludes an explicit separating input. For instance, an ambiguous input807 may include a first portion 827 followed by a separating input 831followed by a second portion 835. In such a circumstance, thedisambiguation routine will seek to identify a language object 100 thatcorresponds with the second portion 835 of the ambiguous input 807regardless of whether a language object 100 was identified thatcorresponds with and has a length equal to the length of the firstportion 827. In other words, the user signals to the disambiguationroutine that the first portion 827 is to be treated as a first componentof a compound language input, and such signal is provided by the user bythe inputting of the separating input 831. It is noted that such aseparating input 831 can be provided by the user whether a languageobject 100 was identified that corresponds with and has a length equalto the first portion 827, whether no such language object 100 wasidentified, and/or whether the output for the first portion 827 was theresult of an artificial variant.

While specific embodiments of the disclosed and claimed concept havebeen described in detail, it will be appreciated by those skilled in theart that various modifications and alternatives to those details couldbe developed in light of the overall teachings of the disclosure.Accordingly, the particular arrangements disclosed are meant to beillustrative only and not limiting as to the scope of the disclosed andclaimed concept which is to be given the full breadth of the claimsappended and any and all equivalents thereof.

1. A method of disambiguating an input into a handheld electronic devicehaving an input apparatus, an output apparatus, and a memory havingstored therein a plurality of objects comprising a plurality of languageobjects, the input apparatus including a plurality of input members,each of at least some of the input members having a plurality oflinguistic elements assigned thereto, the method comprising: detectingan ambiguous input; generating a number of compound language solutionsby, for each compound language solution: identifying a language objectcorresponding with a portion of the ambiguous input and having a lengthequal to the length of the portion, and for each of a number of otherportions of the ambiguous input, identifying another language objectcorresponding with the other portion of the ambiguous input; outputtinga representation of each of at least some of the compound languagesolutions, each said representation comprising a representation of thelanguage object and, for each of the other portions, a representation ofat least a portion of the another language object correspondingtherewith; and arranging at least some of the representations in orderof decreasing priority according to the increasing quantity of otherportions.
 2. The method of claim 1 wherein a first compound languagesolution has a first quantity of other portions, and wherein a secondcompound language solution has a second quantity of other portions equalto the first quantity, and further comprising calculating a lengthidentity value for each of the first and second compound languagesolutions.
 3. The method of claim 2, further comprising arranging therepresentations of the first and second compound language solutions inorder of decreasing priority according to the increasing value of thecalculated length identity value.
 4. The method of claim 2 wherein theplurality of objects further comprise a plurality of frequency objects,each of at least some of the language objects being associated with anassociated frequency object, wherein the length identity values for thefirst and second compound language solutions are equal, furthercomprising calculating for the first compound language solution anaverage frequency value comprised of an average of the frequency valuesassociated with the language object and the number of another languageobjects of the first compound language solution, calculating for thesecond compound language solution an average frequency value comprisedof an average of the frequency values associated with the languageobject and the number of another language objects of the second compoundlanguage solution, and arranging the representations of the first andsecond compound language solutions in order of decreasing priorityaccording to the decreasing value of the average frequency value.
 5. Themethod of claim 2, further comprising calculating as the length identityvalue of the first compound language solution a sum of each differencein length between one of the portion and the number of other portionsthereof having a given length and the one of the portion and the numberof other portions thereof having a next greatest length shorter than thegiven length, calculating as the length identity value of the secondcompound language solution a sum of each difference in length betweenone of the portion and the number of other portions thereof having agiven length and the one of the portion and the number of other portionsthereof having a next greatest length shorter than the given length, andarranging the representations of the first and second compound languagesolutions in order of decreasing priority according to the increasingvalue of the calculated length identity value of the first and secondcompound language solutions.
 6. A handheld electronic device comprising:an input apparatus comprising a plurality of input members, each of atleast some of the input members having a plurality of linguisticelements assigned thereto; an output apparatus; a processor apparatuscomprising a processor and a memory, the memory having stored therein aplurality of objects comprising a plurality of language objects, theprocessor apparatus being structured to detecting an ambiguous input,and being further structured to generate a number of compound languagesolutions by, for each compound language solution: identifying alanguage object corresponding with a portion of the ambiguous input andhaving a length equal to the length of the portion, and for each of anumber of other portions of the ambiguous input, identifying anotherlanguage object corresponding with the other portion of the ambiguousinput; the output apparatus being structured to output a representationof each of at least some of the compound language solutions, each saidrepresentation comprising a representation of the language object and,for each of the other portions, a representation of at least a portionof the another language object corresponding therewith; and theprocessor apparatus being structured to arrange at least some of therepresentations in order of decreasing priority according to theincreasing quantity of other portions.
 7. The handheld electronic deviceof claim 6 wherein the processor apparatus is structured to generate afirst compound language solution having a first quantity of otherportions and to generate a second compound language solution having asecond quantity of other portions equal to the first quantity, theprocessor apparatus being further structured to calculate a lengthidentity value for each of the first and second compound languagesolutions.
 8. The handheld electronic device of claim 7 wherein theprocessor apparatus is structured to arrange the representations of thefirst and second compound language solutions in order of decreasingpriority according to the increasing value of the calculated lengthidentity value of the first and second compound language solutions. 9.The handheld electronic device of claim 7 wherein the plurality ofobjects further comprise a plurality of frequency objects, each of atleast some of the language objects being associated with an associatedfrequency object, wherein the length identity values for the first andsecond compound language solutions are equal, the processor apparatusbeing structured to calculate for the first compound language solutionan average frequency value comprised of an average of the frequencyvalues associated with the language object and the number of anotherlanguage objects of the first compound language solution, and beingfurther structured to calculate for the second compound languagesolution an average frequency value comprised of an average of thefrequency values associated with the language object and the number ofanother language objects of the second compound language solution, theprocessor apparatus additionally being structured to arrange therepresentations of the first and second compound language solutions inorder of decreasing priority according to the decreasing value of theaverage frequency value.
 10. The handheld electronic device of claim 7wherein the processor apparatus is structured to calculate as the lengthidentity value of the first compound language solution a sum of eachdifference in length between one of the portion and the number of otherportions thereof having a given length and the one of the portion andthe number of other portions thereof having a next greatest lengthshorter than the given length, is further structured to calculate as thelength identity value of the second compound language solution a sum ofeach difference in length between one of the portion and the number ofother portions thereof having a given length and the one of the portionand the number of other portions thereof having a next greatest lengthshorter than the given length, and is additionally structured to arrangethe representations of the first and second compound language solutionsin order of decreasing priority according to the increasing value of thecalculated length identity value of the first and second compoundlanguage solutions.